A variety of image processing systems for compressing image data and decoding the compressed data to display decoded image data have been proposed with the increased demands for digital images. Extremely high-speed image processing and image reading allow image data, recorded in a compressive form, to be reconstructed in a much more efficient manner. However, for large images, the ability to transfer and process an image may be hampered unless it is efficiently compressed.
Known processes of image data compression include orthogonal transform coding, discrete cosine transform (DCT) coding, and Huffman coding. A known image coding and compressing method by orthogonal transform is an H.261 image coding process of CCITT (Comite Counsultatif International Telegraphique et Telephonique). An example of DCT compression for color images is an image coding method based on a J-PEG (Joint Photographics Expert Group) algorithm d.
In conventional image compression processes, image data is coded in block units according to an irreversible transform where original image data is not reconstructed perfectly by decoding. Continuity of an original image may thus be interrupted undesirably on a boundary of adjacent blocks. An interblock distortion removal filter is conventionally used to eliminate such discontinuity. This filter stores decoded video data and executes a filter operation or, more concretely, calculates a weighted average of data of adjacent pixels while reading data of adjacent blocks.
The conventional image processing and decoding systems described above have problems in efficiently handling upsampled continuous tone (“contone”) images, especially upsampled contone images that incorporate linework in the image as well. Upsampling is the process of taking an original image and increasing the image by taking a single pixel and turning it into an image component of “n×n” pixels. For example, a single pixel may be tripled in the horizontal and vertical axes to create a 9-pixel (3×3) upsampled image component. One interesting image type, which includes both contone and linework data, is an image that includes upsampled contone backgrounds with linework details. The upsampled contone background includes a significant quantity of redundant information. However, the linework details generally do not have the same type of redundant information. Processing uncompressed images with both upsampled and linework data can be difficult for the reasons noted above with regard to large images. Accordingly, there is a need for an efficient compression algorithm for upsampled images that includes both contone and linework data.